Abstract
BackgroundProtein-protein interactions (PPIs) play fundamental roles in nearly all biological processes, and provide major insights into the inner workings of cells. A vast amount of PPI data for various organisms is available from BioGRID and other sources. The identification of communities in PPI networks is of great interest because they often reveal previously unknown functional ties between proteins. A large number of global clustering algorithms have been applied to protein networks, where the entire network is partitioned into clusters. Here we take a different approach by looking for local communities in PPI networks.ResultsWe develop a tool, named Local Protein Community Finder, which quickly finds a community close to a queried protein in any network available from BioGRID or specified by the user. Our tool uses two new local clustering algorithms Nibble and PageRank-Nibble, which look for a good cluster among the most popular destinations of a short random walk from the queried vertex. The quality of a cluster is determined by proportion of outgoing edges, known as conductance, which is a relative measure particularly useful in undersampled networks. We show that the two local clustering algorithms find communities that not only form excellent clusters, but are also likely to be biologically relevant functional components. We compare the performance of Nibble and PageRank-Nibble to other popular and effective graph partitioning algorithms, and show that they find better clusters in the graph. Moreover, Nibble and PageRank-Nibble find communities that are more functionally coherent.ConclusionThe Local Protein Community Finder, accessible at http://xialab.bu.edu/resources/lpcf, allows the user to quickly find a high-quality community close to a queried protein in any network available from BioGRID or specified by the user. We show that the communities found by our tool form good clusters and are functionally coherent, making our application useful for biologists who wish to investigate functional modules that a particular protein is a part of.
Highlights
Protein-protein interactions (PPIs) play fundamental roles in most biological processes, and provide major insights into the inner workings of cells
The Local Protein Community Finder, accessible at http:/ /xialab.bu.edu/resources/lpcf, allows the user to find local communities in any protein network available from BioGRID [38], which is specified by an organism and a set of interaction types
If the user would like to use a network that is not from BioGRID, the generic Local Community Finder can be used instead, available at http://xialab.bu.edu/ resources/lcf, where one can upload any undirected network in edge-list format
Summary
Protein-protein interactions (PPIs) play fundamental roles in most biological processes, and provide major insights into the inner workings of cells. The identification of communities in PPI networks is of great interest because they often reveal previously unknown functional ties between proteins. Using the link structure of a network to gain insight into the function of its nodes is a ubiquitous technique in biological, social, and computer networks [1,2,3,4,5,6,7,8,9,10,11]. Finding large cliques in a graph, which are subsets of nodes that are completely connected, is a well-studied problem It is computationally infeasible for large networks: finding the size of the largest clique in a graph is NP-Complete [12], and approximating it is hard as well [13]. Many heuristic methods have been developed, which look for defective cliques (cliques that are missing some edges), or more generally dense components
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